Traffic Data Collection and Visualization Using Intelligent Transport Systems

Abstract

Traffic conditions nowadays are in a grim situation caused by daily congestion and accidents. Thus, traffic state forecasting is considered as one of the most important traffic management techniques on roadway networks. Owing to financial and economic constraints, uses of sensors and cameras along the road are not a feasible option. Henceforth, probe vehicles equipped with GPS and other sensors are gaining prominence and are frequently used in developed countries to collect traffic data. In the probe vehicle concept, vehicles themselves are acting as roving traffic detectors, which are not bound to specific and fixed locations along the road infrastructure. In this paper, a sensor fusion model based on the extended Kalman filter and measurement inputs from a global positioning system (GPS) receiver and inertial measurement unit (IMU) sensors to improve absolute position estimation and to collect traffic data using ultrasonic sensors and dashcam has been presented. The proposed methodology has been tested for prevailing mixed traffic conditions in Prayagraj city. On the basis of the analysis of collected data, this paper presents a systematic solution to efficiently estimate the traffic state of large-scale urban road networks.